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Upstart

Runs an AI lending marketplace for consumers

Senior Engineering Manager - Machine Learning Data Enablement

Engineering LeadershipConfirmed live in the last 24 hours
Full-timeRemote in USARemote

About the Job

Requirements

  • A bachelor's degree in Computer Science, Engineering, or Mathematics, or a related field (or its equivalent), and 8 years of engineering experience, including at least 3 years of direct people management experience.
  • Experience owning production data pipelines that enable both offline training and online inference.
  • Experience building and scaling data systems in modern stacks such as Databricks/Spark, Python, SQL, AWS, streaming systems, and orchestration frameworks, as well as distributed systems architecture.
  • Experience owning complex cross-functional initiatives across engineering, machine learning, and business stakeholders, including delivery under peer pushback and dependency negotiation.
  • Experience designing and enforcing data quality frameworks and observability for production systems, including reconciliation, drift detection, and incident/postmortem operating loops.

Responsibilities

  • Build and lead a high-performing team spanning data integration, data quality, metadata, and machine-learning-critical data infrastructure for online inference and offline training, including establishing dedicated integration capacity where needed.
  • Set and execute technical strategy aligned to measurable north-star metrics, including increasing data evaluation velocity and reducing time to production.
  • Drive data quality and reconciliation frameworks, including retrospective-versus-production checks, ingress-level monitoring, and drift detection to prevent launch issues and downstream model degradation.
  • Champion company-wide adoption of data contracts and service-level agreements, ensuring data producers adopt clear ownership, quality standards, and monitoring practices for machine-learning-critical datasets.
  • Establish end-to-end ownership across the third-party and internal data lifecycle, eliminating fragmented workflows and implicit accountability.
  • Accelerate third-party data onboarding by operationalizing standardized vendor intake, secure retrospective ingestion, templated integrations, and configurable microservices to reduce engineering effort and cycle time.
  • Unlock internal data for machine-learning innovation by improving metadata coverage, lineage standards, ownership contracts, and machine-learning discoverability across high-impact internal domains.

Desired Qualifications

  • 10 or more years in data engineering and machine learning platform or machine learning data platform roles, with 5 or more years managing engineering teams.
  • Experience with feature stores and real-time feature delivery, or equivalent feature transformation interfaces used in inference.
  • Strong knowledge of lakehouse architecture and big data processing frameworks.
  • Familiarity with DevOps and infrastructure-as-code practices, including Kubernetes, Terraform, and continuous integration/continuous delivery.
  • Experience in fintech or other regulated environments where explainability, auditability, and controls matter.
  • Ability to translate complex technical tradeoffs into business impact and influence cross-functional strategy.

About the Company

Upstart

Runs an AI lending marketplace for consumers

Upstart runs an online lending marketplace where banks and credit unions use its AI models to decide which consumers get loans and at what rates. In 2017, it received the first no-action letter issued by the Consumer Financial Protection Bureau, a statement that the regulator had no plans to act against its AI lending model. Borrowers apply through Upstart or a partner lender's website for personal loans, auto loans, home equity lines of credit, and Cash Line, a revolving line of credit. Lenders pay Upstart fees for referring and approving borrowers and for collecting payments, while partner lenders and investment funds put up most of the money lent. Upstart was founded in 2012 and is based in Burlingame, California.

Company size
1,001-5,000
Company stage
IPO
Headquarters
Burlingame, CA
Founded
2012

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